2021
DOI: 10.1109/tmi.2021.3081396
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Domain Adaptation-Based Deep Learning for Automated Tumor Cell (TC) Scoring and Survival Analysis on PD-L1 Stained Tissue Images

Abstract: We report the ability of two deep learningbased decision systems to stratify non-small cell lung cancer (NSCLC) patients treated with checkpoint inhibitor therapy into two distinct survival groups. Both systems analyze functional and morphological properties of epithelial regions in digital histopathology whole slide images stained with the SP263 PD-L1 antibody. The first system learns to replicate the pathologist assessment of the Tumor Cell (TC) score with a cut-point for positivity at 25% for patient strati… Show more

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Cited by 26 publications
(20 citation statements)
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“…A completely different approach attempts to directly map PD-L1 scores using Deep Learning [16,35], which might allow it to be less vulnerable to artifacts. Using an auxiliary classifier generative adversarial network for tumor detection, the TPS could be calculated based on the ratio of the pixel numbers of positive and negative tumor areas.…”
Section: Discussionmentioning
confidence: 99%
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“…A completely different approach attempts to directly map PD-L1 scores using Deep Learning [16,35], which might allow it to be less vulnerable to artifacts. Using an auxiliary classifier generative adversarial network for tumor detection, the TPS could be calculated based on the ratio of the pixel numbers of positive and negative tumor areas.…”
Section: Discussionmentioning
confidence: 99%
“…Using an auxiliary classifier generative adversarial network for tumor detection, the TPS could be calculated based on the ratio of the pixel numbers of positive and negative tumor areas. Generative adversarial networks basically consist of two neural networks (discriminator and generator), which compete with each other and allow the model to learn [16,35]. Although this deep learning approach was able to determine the TPS in entire WSIs, the interpretability of the underlying decision criteria was not provided.…”
Section: Discussionmentioning
confidence: 99%
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“…147 For kidney cancers, segmenting glomeruli 126,148 or different subtypes of kidney tissues 127,149 are key tasks of interest. Researchers have also made progress on segmenting normal and abnormal parts from histopathological images for breast, 90,99,150 lung, 110,151 bladder, 134 stomach, 123 prostate 90 cancer.…”
Section: Segmentationmentioning
confidence: 99%
“…These are then clustered to automate the identification of subgroups in lung, 159 brain, 159 breast, 98,181 colon cancer 113 WSIs. In addition, research has begun to use ML to learn associations between gene expression and pathological images, 110,130,182 perform stain normalization for histopathological images, 106,[183][184][185][186] generate synthetic data, 106,130,148,151,170 compress images 187 and automate histopathological captioning and diagnosis generation. 135…”
Section: Countingmentioning
confidence: 99%